Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove cloudy and precipitation-affected observations. Historical samples and physical ensembles are combined to construct four-dimensional ensembles according to the observed frequency of the Himawari-8 satellite. The purpose of this study was to test the potential impacts of assimilating high temporal resolution observations with POD-4DEnVar in a numerical weather prediction (NWP) system. Two parallel experiments were performed with and without Himawari-8 radiance assimilation during the entire month of July 2020. The results of the experiment with radiance assimilation show that it improves the analysis and forecast accuracy of geopotential, horizontal wind field and relative humidity compared to the experiment without radiance assimilation. Moreover, the equitable threat score (ETS) of 24-h accumulated precipitation shows that assimilating Himawari-8 radiance improves the rainfall forecast accuracy. Improvements were found in the structure, amplitude and location of the precipitation. In addition, the ETS of hourly accumulated precipitation indicates that assimilating high temporal resolution Himawari-8 radiance can improve the prediction of rapidly developed rainfall. Overall, assimilating every-10-min AHI radiance from Himawari-8 with POD-4DEnVar has positive impacts on NWP.

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis.

Data assimilation generally assumes that observation-minus-background (OMB) departures follow an unbiased Gaussian distribution. Therefore, bias correction (BC) is essential for the effective assimilation of satellite radiances. This study applies four machine learning (ML) approaches to reduce systematic biases in clear-sky observations from the water-vapor-sensitive channels 9 and 10 of the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI). These methods include random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and the Kolmogorov–Arnold network (KAN). The machine-learning-based bias correction (MLBC) methods are evaluated for Typhoon In-Fa (2021) using the Weather Research and Forecasting Data Assimilation (WRFDA) system. Experiments without BC and with conventional variational bias correction (VarBC) are designed for comparison. Compared with VARBC, the four MLBC methods better capture nonlinear OMB biases and improve the statistical distributions of the departures. They produce more symmetric OMB distributions for channel 9 and distributions closer to the Gaussian distribution for channel 10, with smaller OMB STDVs and RMSEs than VARBC over both land and ocean for channel 10. After assimilation, KANBC produces the smallest OMA STDV and RMSE for both channels. For the Typhoon In-Fa case examined here, KANBC yields the smallest track errors among the experiments. In particular, KAN-based BC reduces the anomalously strong positive 500 hPa geopotential height increments over the continental high pressure and western North Pacific subtropical high pressure. The resulting initial conditions improve the environmental steering flow and reduce the northwestward track bias. Channel sensitivity experiments were further conducted for water-vapor-sensitive channels 9 and 10 of the FY-4A AGRI using the KAN-based BC method. The results show that single-channel assimilation performs better than dual-channel assimilation. Assimilating channel 10 alone produces the smallest track errors and lower analysis RMSEs relative to ERA5 than assimilating both channels.